Evaluating Population Forecast Accuracy: A Regression Approach Using County Data.

Many studies have evaluated the impact of differences in population size and growth rate on population forecast accuracy. Virtually all these studies have been based on aggregate data; that is, they focused on average errors for places with particular size or growth rate characteristics. In this study, we take a different approach by investigating forecast accuracy using regression models based on data for individual places. Using decennial census data from 1900 to 2000 for 2,482 counties in the US, we construct a large number of county population forecasts and calculate forecast errors for 10- and 20-year horizons. Then, we develop and evaluate several alternative functional forms of regression models relating population size and growth rate to forecast accuracy; investigate the impact of adding several other explanatory variables; and estimate the relative contributions of each variable to the discriminatory power of the models. Our results confirm several findings reported in previous studies but uncover several new findings as well. We believe regression models based on data for individual places provide powerful but under-utilized tools for investigating the determinants of population forecast accuracy.

Empirical Prediction Intervals for County Population Forecasts.

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.

Fleeing the storm(s): an examination of evacuation behavior during Florida's 2004 hurricane season.

The 2004 hurricane season was the worst in Florida's history, with four hurricanes causing at least 47 deaths and some $45 billion in damages. To collect information on the demographic impact of those hurricanes, we surveyed households throughout the state and in the local areas that sustained the greatest damage. We estimate that one-quarter of Florida's population evacuated prior to at least one hurricane; in some areas, well over one-half of the residents evacuated at least once, and many evacuated several times. Most evacuees stayed with family or friends and were away from home for only a few days. Using logistic regression analysis, we found that the strength of the hurricane and the vulnerability of the housing unit had the greatest impact on evacuation behavior; additionally, several demographic variables had significant effects on the probability of evacuating and the choice of evacuation lodging (family/friends, public shelters, or hotels/motels). With continued population growth in coastal areas and the apparent increase in hurricane activity caused by global warming, threats posed by hurricanes are rising in the United States and throughout the world. We believe the present study will help government officials plan more effectively for future hurricane evacuations.

Snowbirds, sunbirds, and stayers: seasonal migration of elderly adults in Florida.

OBJECTIVE: Most migration statistics in the United States focus on changes in place of usual residence, thereby missing temporary moves such as business trips, vacations, and seasonal migration. In this article, we analyze the temporary in- and out-migration of elderly adults in Florida. Our primary objectives are to develop a methodology for estimating the number of temporary migrants and to analyze their demographic characteristics. METHODS: Using survey data, we estimated the number, timing, and duration of temporary moves and the origins, destinations, and characteristics of elderly temporary migrants. We compared the characteristics of temporary in-migrants, out-migrants, and non-migrants, and we used logistic regression analysis in order to evaluate differences in those characteristics. RESULT: We estimate that Florida had more than 800,000 elderly temporary in-migrants and more than 300,000 elderly temporary out-migrants at peak times in 2005. Income, education, employment, and health status were among the major determinants of temporary migration. DISCUSSION: The temporary migration of elderly adults has a major impact on the resident populations of both sending and receiving communities. This article presents a methodology for estimating temporary migration and provides insights into migratory patterns that cannot be achieved by focusing solely on changes in place of usual residence.

An evaluation of population projections by age.

A number of studies have evaluated the accuracy of projections of the size of the total population, but few have considered the accuracy of projections by age group. For many purposes, however, the relevant variable is the population of a particular age group, rather than the population as a whole. We investigated the precision and bias of a variety of age-group projections at the national and state levels in the United States and for counties in Florida. We also compared the accuracy of state and county projections that were derived from full-blown applications of the cohort-component method with the accuracy of projections that were derived from a simpler, less data-intensive version of the method. We found that age-group error patterns are different for national projections than for subnational projections; that errors are substantially larger for some age groups than for others; that differences in errors among age groups decline as the projection horizon becomes longer; and that differences in methodological complexity have no consistent impact on the precision and bias of age-group projections.

A regression approach to estimating the average number of persons per household.

In the housing unit method, population is calculated as the number of households times the average number of persons per household (PPH), plus the population residing in group quarters facilities. Estimates of households and the group quarters population can be derived directly from concurrent data series, but estimates of PPH have traditionally been based on previous values or estimates for larger areas. In our study, we developed several regression models in which PPH estimates were based on symptomatic indicators of PPH change. We tested these estimates using county-level data in four states and found them to be more precise and less biased than estimates based on more commonly used methods.

Demographic effects of natural disasters : A case study of hurricane Andrew

Many studies have considered the economic, social, and psychological effects of hurricanes, earthquakes, floods, tornadoes, and other natural disasters, but few have considered their demographic effects. In this paper we describe and evaluate a method for measuring the effects of hurricane Andrew on the housing stock and population distribution in Dade County, Florida. Using information collected through sample surveys and from other data sources, we investigate the extent of housing damages, the number of people forced out of their homes, where they went, how long they stayed, and whether they returned to their prehurricane residences. We conclude that more than half the housing units in Dade County were damaged by Hurricane Andrew; that more than 353,000 people were forced to leave their homes, at least temporarily; and that almost 40,000 people left the county permanently as a direct result of the hurricane. We delieve that this study will provide methodological guidance to analysis studying the demographic effects of other large-scale natural disasters.(AU)